Self-Supervised Algorithm for Predicting Data Based on Knitting Direction in Capacitive Strain Stitch Sensors 


Vol. 26,  No. 5, pp. 2221-2231, May  2025
10.1007/s12221-025-00942-z


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  Abstract

In this study, the characteristics of knit-based stitch sensors were compared using capacitance and stress values based on the knit direction. The restored predicted data were analyzed using a masked autoencoder algorithm. First, changes in capacitance with respect to strain were compared, followed by a comparison of tress values with respect to strain. The capacitance (C) values reflected differences in the distance between electrodes (d) and the area of the electrodes (A) , with the course direction showing a larger variation. Although the stress values ( ) exhibited similar trends in the graph, the comparison using the elastic modulus (E) and oisson's ratio ( ) confirmed that the course direction had greater deformation under the same tensile strain. Finally, using the masked autoencoder algorithm, the restoration rate of the original data was measured under 10%, 30%, 50%, and 100% noise levels, showing nearly perfect consistency. These results demonstrate the analysis of knit stitch sensor characteristics and the performance evaluation of the masked autoencoder algorithm.

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  Cite this article

[IEEE Style]

J. Kim and J. Kim, "Self-Supervised Algorithm for Predicting Data Based on Knitting Direction in Capacitive Strain Stitch Sensors," Fibers and Polymers, vol. 26, no. 5, pp. 2221-2231, 2025. DOI: 10.1007/s12221-025-00942-z.

[ACM Style]

Ji-seon Kim and Jooyong Kim. 2025. Self-Supervised Algorithm for Predicting Data Based on Knitting Direction in Capacitive Strain Stitch Sensors. Fibers and Polymers, 26, 5, (2025), 2221-2231. DOI: 10.1007/s12221-025-00942-z.

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